Deep semi-supervised learning is a fast-growing field with a range of practical applications.
This paper provides a comprehensive survey on both fundamentals and recent advances in …

Medical image segmentation is a fundamental and critical step in many image-guided
clinical approaches. Recent success of deep learning-based segmentation methods usually …

The recently proposed FixMatch achieved state-of-the-art results on most semi-supervised
learning (SSL) benchmarks. However, like other modern SSL algorithms, FixMatch uses a …

Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data
to improve a model's performance. This domain has seen fast progress recently, at the cost …

One paradigm for learning from few labeled examples while making best use of a large
amount of unlabeled data is unsupervised pretraining followed by supervised fine-tuning …

Semi-supervised learning has proven to be a powerful paradigm for leveraging unlabeled
data to mitigate the reliance on large labeled datasets. In this work, we unify the current …

Training generative adversarial networks (GAN) using too little data typically leads to
discriminator overfitting, causing training to diverge. We propose an adaptive discriminator …

Semi-supervised learning is the branch of machine learning concerned with using labelled
as well as unlabelled data to perform certain learning tasks. Conceptually situated between …

The crux of semi-supervised semantic segmentation is to assign pseudo-labels to the pixels
of unlabeled images. A common practice is to select the highly confident predictions as the …

Semi-supervised learning lately has shown much promise in improving deep learning
models when labeled data is scarce. Common among recent approaches is the use of …